Analyzing Space - Spatial Data Science Methods

Presentation Description

Spatial data science uses many of the same techniques and algorithm as traditional data science, but the spatial component can add a large amount of additional information by combining with other sources at the same location (e.g., census, geolocated tweets), using real-time routing services, or using the spatial structure of the distribution of the data. In this talk, I will highlight the work we have done in linear optimization, genetic algorithms, and constraint-based clustering that take special advantage of the spatial part of the data. For example, using the Python package CVXOPT, we solved a linear optimization problem that optimally distributes an asset from a source to a drain according to the road network and constraints that the drains cannot be over capacity, occasionally have fixed assignments, and all the asset has to be moved.